An entity, such as enterprise, may want to analyze or “mine” large amounts of data, such as text data. For example, an enterprise might want to analyze tens of thousands of text files to look for patterns (e.g., so that predictions can be made and/or resources may be allocated in appropriate ways). Note that an entity might analyze this data in connection with different purposes, and, moreover, different purposes may need to analyze the data in different ways. For example, a single acronym might refer to one thing when it appears in one type of document and different thing when it appears in a different type of document. It can be difficult to identify patterns across such large amounts of data and different purposes. In addition, manually managing the different needs and requirements (e.g., different logic rules) associated with different purposes can be a time consuming and error prone process.
Note that electronic records may be used to store information for an enterprise. Moreover, it may be advantageous for an enterprise to correctly predict future values that might be associated with each electronic record (e.g., so that decisions can be made as appropriate). The future value of some types of information may be predictable with a high degree of certainty. For other types of information, however, the confidence an enterprise can have in predicting the future value (or values) may be much lower. The propensity for a value to differ from its predicted value is referred to herein as “volatility.” In some cases, text based characteristics and/or patterns associated with an electronic might be indicative of volatility.
Identification and proper handling of electronic records with high volatility potential may allow for improved alignment of resources. Thus, there is a need in the art for methods and systems using text mining to identify highly volatile data values. In addition, there is a need in the art for methods and systems of addressing these values.